Biometric authentication systems are now commonplace. Most biometric imaging systems relate a sensed biometric image and a known biometric template, one to the other. Such a system is referred to as a one-to-one authentication system. Using such a system, a sensed image is generally analysed within a frame of reference common to the frame of reference in which the template was extracted. From the analysed sensed biometric data, feature data is extracted within the known frame of reference. This extracted feature data is then registered against the biometric template.
Another form of biometric authentication system relies on a one-to-many comparison process. In the one to many authentication process, a sensed image is generally analysed within a frame of reference common to the frame of reference in which all of the templates were extracted. From the analysed sensed biometric data, feature data is extracted within the known frame of reference. This extracted feature data is then registered against each of the biometric templates. The most close match is then selected as the “authentication.” Unfortunately, in such a system, the registration process is either more computationally expensive resulting in better authentication or of a less computationally expensive nature providing for faster execution.
One method to speed up the process of registration in a one-to-many biometric authentication system involves dividing the biometric templates based on characteristics and then, by identifying the characteristics, only registering the feature data against the biometric templates having similar characteristics. For example, fingerprints are groupable based on the fingerprint type—loop, swirl, etc. Thus, registration of the feature data is only performed against some of the template. Unfortunately, some feature data is difficult to classify resulting in less of an advantage to the above method than might be expected. Further, it is difficult to group fingerprints into small enough groupings due to the general nature of fingerprint classification and difficulties in accurately classifying fingerprints.
Also, the use of a subset of, for example, a fingerprint image as a PIN is difficult. Fingerprints and other biometric information sources are not truly repeatable in nature. A fingertip may be drier or wetter. It may be more elastic or less. It may be scratched or dirty or clean. Each of the above listed conditions affects the fingerprint image and, as such, means that the image subset may very well differ. Typical PIN analysis requires provision of the unique and static PEN. Here, such a method will result in a system that is very inconvenient to use.
It is an object to provide a method of identifying an individual that overcomes the limitations of the prior art.